def test_array_with_nans(self):
x1 = self.rs.rand(10, 10)
nulls = np.zeros(10) * np.nan
x2 = np.c_[x1, nulls]
m1 = mat._HeatMapper(x1, **self.default_kws)
m2 = mat._HeatMapper(x2, **self.default_kws)
assert m1.vmin == m2.vmin
assert m1.vmax == m2.vmax
def test_ticklabels_off(self):
kws = self.default_kws.copy()
kws['xticklabels'] = False
kws['yticklabels'] = False
p = mat._HeatMapper(self.df_norm, **kws)
assert p.xticklabels == []
assert p.yticklabels == []
def test_robust_vlims(self):
kws = self.default_kws.copy()
kws["robust"] = True
p = mat._HeatMapper(self.df_unif, **kws)
assert p.vmin == np.percentile(self.x_unif, 2)
assert p.vmax == np.percentile(self.x_unif, 98)
def test_mask_limits(self):
"""Make sure masked cells are not used to calculate extremes"""
kws = self.default_kws.copy()
mask = self.x_norm > 0
kws['mask'] = mask
p = mat._HeatMapper(self.x_norm, **kws)
assert p.vmax == np.ma.array(self.x_norm, mask=mask).max()
assert p.vmin == np.ma.array(self.x_norm, mask=mask).min()
mask = self.x_norm < 0
kws['mask'] = mask
p = mat._HeatMapper(self.x_norm, **kws)
assert p.vmin == np.ma.array(self.x_norm, mask=mask).min()
assert p.vmax == np.ma.array(self.x_norm, mask=mask).max()
def test_custom_sequential_vlims(self):
kws = self.default_kws.copy()
kws["vmin"] = 0
kws["vmax"] = 1
p = mat._HeatMapper(self.df_unif, **kws)
assert p.vmin == 0
assert p.vmax == 1
def test_custom_ticklabels(self):
kws = self.default_kws.copy()
xticklabels = list('iheartheatmaps'[:self.df_norm.shape[1]])
yticklabels = list('heatmapsarecool'[:self.df_norm.shape[0]])
kws['xticklabels'] = xticklabels
kws['yticklabels'] = yticklabels
p = mat._HeatMapper(self.df_norm, **kws)
assert p.xticklabels == xticklabels
assert p.yticklabels == yticklabels
def test_centered_vlims(self):
kws = self.default_kws.copy()
kws["center"] = .5
p = mat._HeatMapper(self.df_unif, **kws)
assert p.vmin == self.df_unif.values.min()
assert p.vmax == self.df_unif.values.max()
def test_custom_diverging_vlims(self):
kws = self.default_kws.copy()
kws["vmin"] = -4
kws["vmax"] = 5
kws["center"] = 0
p = mat._HeatMapper(self.df_norm, **kws)
assert p.vmin == -4
assert p.vmax == 5
def test_df_multindex_input(self):
df = self.df_norm.copy()
index = pd.MultiIndex.from_tuples([("A", 1), ("B", 2), ("C", 3),
("D", 4)],
names=["letter", "number"])
index.name = "letter-number"
df.index = index
p = mat._HeatMapper(df, **self.default_kws)
combined_tick_labels = ["A-1", "B-2", "C-3", "D-4"]
npt.assert_array_equal(p.yticklabels, combined_tick_labels)
assert p.ylabel == "letter-number"
p = mat._HeatMapper(df.T, **self.default_kws)
npt.assert_array_equal(p.xticklabels, combined_tick_labels)
assert p.xlabel == "letter-number"
def test_mask_input(self, dtype):
kws = self.default_kws.copy()
mask = self.x_norm > 0
kws['mask'] = mask
data = self.x_norm.astype(dtype)
p = mat._HeatMapper(data, **kws)
plot_data = np.ma.masked_where(mask, data)
npt.assert_array_equal(p.plot_data, plot_data)
def test_df_input(self):
p = mat._HeatMapper(self.df_norm, **self.default_kws)
npt.assert_array_equal(p.plot_data, self.x_norm)
pdt.assert_frame_equal(p.data, self.df_norm)
npt.assert_array_equal(p.xticklabels, np.arange(8))
npt.assert_array_equal(p.yticklabels, self.letters.values)
assert p.xlabel == ""
assert p.ylabel == "letters"
def test_ndarray_input(self):
p = mat._HeatMapper(self.x_norm, **self.default_kws)
npt.assert_array_equal(p.plot_data, self.x_norm)
pdt.assert_frame_equal(p.data, pd.DataFrame(self.x_norm))
npt.assert_array_equal(p.xticklabels, np.arange(8))
npt.assert_array_equal(p.yticklabels, np.arange(4))
assert p.xlabel == ""
assert p.ylabel == ""
def test_custom_ticklabel_interval(self):
kws = self.default_kws.copy()
xstep, ystep = 2, 3
kws['xticklabels'] = xstep
kws['yticklabels'] = ystep
p = mat._HeatMapper(self.df_norm, **kws)
nx, ny = self.df_norm.T.shape
npt.assert_array_equal(p.xticks, np.arange(0, nx, xstep) + .5)
npt.assert_array_equal(p.yticks, np.arange(0, ny, ystep) + .5)
npt.assert_array_equal(p.xticklabels, self.df_norm.columns[0:nx:xstep])
npt.assert_array_equal(p.yticklabels, self.df_norm.index[0:ny:ystep])
def test_mask(self):
df = pd.DataFrame(data={
'a': [1, 1, 1],
'b': [2, np.nan, 2],
'c': [3, 3, np.nan]
})
kws = self.default_kws.copy()
kws["mask"] = np.isnan(df.values)
m = mat._HeatMapper(df, **kws)
npt.assert_array_equal(np.isnan(m.plot_data.data), m.plot_data.mask)
def test_cmap_with_properties(self):
kws = self.default_kws.copy()
cmap = copy.copy(mpl.cm.get_cmap("BrBG"))
cmap.set_bad("red")
kws["cmap"] = cmap
hm = mat._HeatMapper(self.df_unif, **kws)
npt.assert_array_equal(cmap(np.ma.masked_invalid([np.nan])),
hm.cmap(np.ma.masked_invalid([np.nan])))
kws["center"] = 0.5
hm = mat._HeatMapper(self.df_unif, **kws)
npt.assert_array_equal(cmap(np.ma.masked_invalid([np.nan])),
hm.cmap(np.ma.masked_invalid([np.nan])))
kws = self.default_kws.copy()
cmap = copy.copy(mpl.cm.get_cmap("BrBG"))
cmap.set_under("red")
kws["cmap"] = cmap
hm = mat._HeatMapper(self.df_unif, **kws)
npt.assert_array_equal(cmap(-np.inf), hm.cmap(-np.inf))
kws["center"] = .5
hm = mat._HeatMapper(self.df_unif, **kws)
npt.assert_array_equal(cmap(-np.inf), hm.cmap(-np.inf))
kws = self.default_kws.copy()
cmap = copy.copy(mpl.cm.get_cmap("BrBG"))
cmap.set_over("red")
kws["cmap"] = cmap
hm = mat._HeatMapper(self.df_unif, **kws)
npt.assert_array_equal(cmap(-np.inf), hm.cmap(-np.inf))
kws["center"] = .5
hm = mat._HeatMapper(self.df_unif, **kws)
npt.assert_array_equal(cmap(np.inf), hm.cmap(np.inf))
def test_custom_cmap(self):
kws = self.default_kws.copy()
kws["cmap"] = "BuGn"
p = mat._HeatMapper(self.df_unif, **kws)
assert p.cmap == mpl.cm.BuGn
def test_default_vlims(self):
p = mat._HeatMapper(self.df_unif, **self.default_kws)
assert p.vmin == self.x_unif.min()
assert p.vmax == self.x_unif.max()
